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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Overview
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
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Since the discovery of the two BER pathways, there has been a debate about how a cell chooses one pathway over the other and the factors determining this selection. Numerous in vitro experiments have pointed out multiple determinants for the sub-pathway selection. These are:
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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DeepCorr:基于深度学习的3GS长读的新型错误纠正方法.

Rongshu Wang1, Jianhua Chen1

  • 1Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China.

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PubMed
概括
此摘要是机器生成的。

深度学习工具DeepCorr准确地纠正了PacBio和ONT等第三代测序 (3GS) 技术的长读数中的错误. 这种新的算法可以提高生物分析的数据质量,同时保持读数长度.

关键词:
深度学习是一种深度学习.混合式错误纠正 混合式错误纠正长时间阅读阅读经常性的神经网络.

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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 第三代测序 (3GS) 技术产生了对生物分析至关重要的超长读数.
  • 3GS长读的高错误率阻碍了下游应用.
  • 现有的错误纠正方法在准确性和资源消耗方面存在局限性.

研究的目的:

  • 提出DeepCorr,一个基于深度学习的新算法,用于准确的长读错误纠正.
  • 为了应对PacBio和牛津纳米孔技术 (ONT) 数据中高错误率的挑战.
  • 在各种基因组研究中提高长读的实用性.

主要方法:

  • 开发了DeepCorr,使用循环神经网络 (RNN) 来纠正长读错误.
  • 将错误纠正作为一个多分类任务,利用reads中的长期依赖性.
  • 集成的高精度短读取生成特征向量和标签用于神经网络训练.

主要成果:

  • 在PacBio和ONT长读时,DeepCorr有效地纠正错误.
  • 该算法改进了对齐标识,同时保留了3GS数据的长度优势.
  • 与最先进的方法相比,在基准数据集上表现出卓越的性能,计算资源使用量减少.

结论:

  • DeepCorr提供了一个全面而准确的深度学习解决方案,用于长读错误纠正.
  • 该工具提高了3GS数据的可靠性,用于各种生物分析.
  • DeepCorr提供了一种高效有效的方法来抛光长阅读,即使在短阅读覆盖范围有限的地区.